3-D surface scans are widely used in a number of fields, including medical and dental applications. Detailed 3-D surface scans of a patients mouth have particular value as a planning resource and for tracking patient progress in orthodontics, restoration, prosthesis, and related dental procedures.
For dental imaging and other types of 3-D imaging, it is often the case that multiple views of an object are acquired in order to represent 3-D surface content for the object. Separate views of the object are captured from different perspectives along the object and are then stitched together by imaging software in order to represent 3-D data in the form of a composite 3-D surface. The process of matching or associating the individual 3-D views is sometimes referred to as stitching. As part of view stitching, each individual 3-D view is matched with views of adjacent portions of the subject and has associated translation and rotation characteristics assigned for assembly of the composite 3-D surface.
For dental imaging, a succession of 3-D views is captured by scanning the patient's mouth using an intra-oral camera. 3-D view content is generated, for example, using patterned light methods, such as fringe projection, or by obtaining point cloud data corresponding to each tooth and soft tissue surface, such as using “structure-from-motion” (SFM) imaging technique, a range imaging method that is familiar to those skilled in the image processing arts. Multi-view imaging and some applicable structure-from-motion techniques are described, for example, in U.S. Patent Application Publication No. 2012/0242794 entitled “Producing 3D images from captured 2D video” by Park et al., incorporated herein in its entirety by reference. Alternate methods for acquiring surface image content could also be used, such as methods using depth information from focus data, radio-frequency (RF) triangulation, timed detection sequencing, stereo-vision and other methods.
In processing for view stitching, the imaging system operates on this acquired surface data for each successive scanned view and calculates the needed alignment information (including rotation and translation) that is used for stitching together each of the acquired 3-D views. For various image stitching approaches, reference is made to U.S. Pat. No. 8,600,193 to Kalayeh; to U.S. Pat. Nos. 7,362,890 and 7,551,760 to Scharlack et al.; to U.S. Patent Application Publication No. 2014/0152660 by Lee et al.; to U.S. Patent Application Publication No. 2012/0320052 by Givon.
One recurring problem with existing 3-D view stitching methods relates to error detection for placement of the acquired 3-D views relative to neighboring images. In order to detect and respond to an error in component image placement, the imaging system often requires operator input or even deletion of a poorly matched view segment. Operator-assisted error detection and correction methods are highly undesirable and error prone. More robust and efficient algorithms for automatic detection and correction of image matching errors are needed in order to make 3-D imaging apparatus more powerful and useful for dental imaging and other applications.
Misplacement of a 3-D view in image processing may occur for any of a number of reasons, including timing constraints and incomplete information. The initial stitching decision by the automated assembly process must be provided quickly in order to provide visual feedback with a minimum of delay, for a suitable viewer experience. In addition, only partial information may be available to the stitching algorithm as the data is being collected. Erroneous placement is sometimes apparent only after additional placement relationships have been computed and results displayed. There is thus a need for automated methods for detecting misplacement and related errors in matching adjacent 3-D views and for correcting these errors without requiring operator intervention.